Hierarchical federated learning based on wireless D2D networks

被引：0

作者：

Liu C.-H. ^{[1
]}

Yu G.-D. ^{[1
]}

Liu S.-L. ^{[1
]}

机构：

[1] College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2023年 / 57卷 / 05期

关键词：

decentralized learning; device-to-device communication; federated learning; resource allocation; training acceleration;

D O I：

10.3785/j.issn.1008-973X.2023.05.005

中图分类号：

学科分类号：

摘要：

A hierarchical federated learning framework based on wireless device-to-device (D2D) networks was proposed to solve the problem of large communication resource consumption and limited device computing resources faced by deploying federated learning in wireless networks. Different from the traditional architectures, the hierarchical aggregation was adopted for model training. The architecture performed the intra-cluster aggregation through D2D networks, and each cluster performed the decentralized training at the same time. A cluster head was selected from each cluster to upload the model to the server for global aggregation. The network traffic of the central node was reduced by combining the hierarchical federated learning and decentralized learning. The degree of the vertices in the D2D networks was used to measure the model convergence performance. The head selection and bandwidth allocation were jointly optimized by maximizing the total degree of selected cluster heads. An optimization algorithm based on dynamic programming was designed to obtain the optimal solutions. The simulation results show that compared with the baseline algorithm, the framework can not only effectively reduce the frequency of global aggregation and training time, but also improve the performance of the final model. © 2023 Zhejiang University. All rights reserved.

引用

页码：892 / 899

页数：7

共 9 条

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